1. Field of the Invention
The present invention relates generally to automated question answering and more specifically to computer-based discriminative learning approaches for answering natural-language questions.
2. Introduction
Question answering (QA) is an interactive human-machine process that aims to find a direct answer to a natural language question from a collection of documents or document fragments, which could be the entire internet, customer care FAQs, a corporate website, or a collection of news articles. Unlike state-of-art spoken dialog systems, which are often configured by a hand-crafted dialog flow and designed for completing tens to hundreds of distinct user requests, QA systems are controlled by information provided in unstructured documents and designed to locate answers to natural language requests pertaining to the content of the given unstructured documents. There has been a body of work on question answering in both the computational linguistic literature and in the industry. A variety of knowledge-intensive approaches have been reported in the Computational Linguistics community (e.g. Text Retrieval Conference (TREC), Message Understanding Conference (MUC), Question Answering workshops in ACL and a number of ACM conferences). A few emerging customer-care question/answering systems, such as the Ikea Anna system or the AT&T Consumer Ask Allie are primarily company-specific and focus on establishing a task ontology with manually crafted questions and answers. Even more established general domain question answering systems, such as AskJeeves and the START natural language question answering system, which have evolved over many years, are very limited in their ability to answer questions.
Accordingly, what is needed in the art is an improved way to provide accurate automated answers to general natural language questions.